Leveraging novel automated diagnostic pathways with augmented precision medicine approaches for the early detection and risk stratification of liver disease

Liver disease represents one of the fastest-growing causes of morbidity and mortality in the world. The principle aetiological variants are contained within the new nomenclature of steatotic liver disease (SLD). This encompasses metabolic-dysfunction associated liver disease (MASLD) (including its necroinflammatory variant - metabolic-dysfunction associated steatohepatitis (MASH)), alcohol-related liver disease (ArLD), and viral hepatitis (inc hepatitis c virus - (HCV)) as dominant pathologies. Recent evidence suggests that the Middle East and North African region (MENA) now has some of the highest prevalence of steatotic liver disease in the world with estimates between 38%-56%. There is significant heterogeneity in disease awareness in the region, which leads to suboptimal strategies to assess, diagnose, and implement rationale interventional strategies. There are important factors involved including sex, ethnicity, genetic, and cultural contexts (including rapidly increasing incidence of T2D and obesity) which are to be considered in any attempts to standardise approaches across both Iraq and the wider geographic landscape.

Using expertise developed within the UoD and NHS Tayside we will look to develop specific diagnostic systems derived from our "intelligent LFT/iLFT" model that incorporate some of the aforementioned differences in populations that could be implemented in Iraq to improve diagnosis and risk stratification for development of advanced chronic liver disease (aCLD). Additionally, we would look to further enhance this work using novel risk stratification strategies and biomarkers to further refine a precision medicine approach. The prospective student would benefit from access to a dynamic group of diverse individuals within the wider Dillon group.

Members of the group of integrated in far reaching projects from early detection of liver disease to development of intelligent platforms of diabetes management and basic science work on liver fibrosis. The group has access to unique datasets (which could be developed for machine learning), state-of-the-art laboratory infrastructures, fully integrated research institutes to develop cross-collaboration within genetics, clinical biochemistry and immunology.

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